Machine Learning for Low Resource Settings

We discuss tools, techniques, and experiences that helps us discover how we can turn raw data into measurable improvements in outcomes.

Transcript

1. Peter Lubell-Doughtie CTO & Co-Founder Machine Learning for Low Resource Settings How can we turn raw data into measurable improvements in outcomes?
2. • Improved outcomes ◦ Reduce preventable deaths ◦ Manage workforce • Have impact at scale ◦ Automation ◦ Standards for best practices ◦ Reduce time to implementation ◦ Reduce time to correction • Applies to problems we have ◦ Find fake and exceptional data ◦ Estimate missing data ◦ Triage data by risk ◦ Predict demand and trends Why Use Machine Learning?
3. Data management solutions and platforms
4. Public health and population level interventions
5. Opportunities • Significant much room for improvement • Willingness to experiment Challenges • Current digital data not collected with ML in mind • Data sovereignty often requires on-premises deployments Working with governments and NGOs
6. Opportunities • Flexibility, sometimes due to lack of pre-existing solutions • State-of-the-art open source solutions Challenges • Use ML as a means not an end • Data consistency across different implementations Scaling Data Management Platforms
7. Peter Lubell-Doughtie - peter@ona.io Ephraim Muhia - emuhia@ona.io Thank You! peter@ona.io @peetldee